Given Cont.Joint PDF function find Covariance MATRIX

In summary, the conversation is discussing the calculation of a covariance matrix for a given joint PDF function. The suggested approach involves finding the means and variances of the three variables, as well as the covariances between them, in order to assemble the covariance matrix. It is assumed that the joint PDF function is fx(x1,x2,x3)=2/3(x1+x2+x3) over the range of 0<xi<1 for each variable.
  • #1
circuitman
1
0
Hello Buddies,

I need to calculate "covariance matrix" of the given joint PDF function.

Joint PDF is fx(x1,x2,x3)=2/3(x1+x2+x3)

over S (x1,x2,x3), 0<xi<1, i=1,2,3

How can I calculte the Covariance Matrix?

Thanks
 
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  • #2
There's a lot of symmetry to exploit here. Here is an outline of the brute force approach.

Step 1: Find [tex] E(X_1) [/tex]. What will that tell you about the other 2 means.

Step 2: Find [tex] E(X_1^2) [/tex], from this and Step 1 you can get [itex] \sigma_{X_1}^2 [/itex] (again - what about the other two?)

Step 3: Now get the covariances between [itex] X_1 [/itex] and [itex] X_2 [/itex] and [itex] X_1 [/itex] and [itex] X_3 [/itex].

Once you have these things you can assemble the covariance matrix.

By the way: I'm assuming you meant that

[tex]
f(x_1, x_2, x_3) = \frac 2 3 \left(x_1 + x_2 + x_3\right), \quad 0 < x_1, x_2, x_3 < 1
[/tex]

rather than something like

[tex]
\frac{2}{3(x_1 + x_2 + x_3)}, \quad 0<x_1, x_2, x_3 < 1
[/tex]

since the second version isn't a density function.
 

1. What is a covariance matrix?

A covariance matrix is a mathematical tool used to measure the relationship between multiple variables. It is a square matrix that shows the variances and covariances of the variables, with the variances on the diagonal and the covariances off-diagonal.

2. How is a covariance matrix calculated?

A covariance matrix is calculated by taking the pairwise covariances between the variables in a dataset and arranging them in a matrix form. The covariance between two variables is calculated by multiplying the deviations from the mean of each variable and then dividing by the total number of observations.

3. What does the covariance matrix tell us?

The covariance matrix provides valuable information about the relationships between variables. The variances on the diagonal show the variability of each variable, while the off-diagonal elements show how the variables are related to each other. A positive covariance indicates that the variables tend to move in the same direction, while a negative covariance indicates they tend to move in opposite directions.

4. How is a covariance matrix used in statistics?

A covariance matrix is used in statistics to analyze the relationships between variables and to make predictions. It is often used in multivariate analysis, such as principal component analysis, factor analysis, and linear regression. It can also be used to identify patterns and trends in data and to determine which variables have the most impact on a particular outcome.

5. How does a joint probability density function relate to a covariance matrix?

A joint probability density function (PDF) is a function that describes the probability of multiple random variables taking on specific values. When calculating a covariance matrix, the joint PDF is used to determine the probabilities of each variable occurring simultaneously. This information is then used to calculate the covariance between the variables, which is an important measure of their relationship.

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